Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	141,867
2	Rhode Island	140,836
3	South Dakota	139,119
4	Utah	124,469
5	Tennessee	122,429
6	Arizona	118,980
7	Iowa	116,183
8	Nebraska	114,102
9	Wisconsin	114,021
10	Oklahoma	113,537
11	New Jersey	113,094
12	South Carolina	113,005
13	Arkansas	111,591
14	Delaware	108,240
15	Indiana	108,237
16	Alabama	107,980
17	Kansas	107,163
18	Illinois	106,543
19	New York	105,897
20	Idaho	105,490
21	Mississippi	105,072
22	Florida	104,942
23	Minnesota	103,316
24	Nevada	102,945
25	Montana	102,520
26	Georgia	101,851
27	Wyoming	100,971
28	Kentucky	100,766
29	Massachusetts	100,632
30	Texas	100,236
31	Louisiana	99,123
32	Missouri	98,435
33	Connecticut	95,804
34	Michigan	95,242
35	California	94,926
36	New Mexico	94,800
37	North Carolina	93,805
38	Alaska	93,107
39	Ohio	92,285
40	Pennsylvania	91,282
41	Colorado	90,244
42	West Virginia	86,463
43	Virginia	77,738
44	Maryland	74,578
45	New Hampshire	70,515
46	District of Columbia	68,070
47	Washington	54,083
48	Puerto Rico	52,390
49	Maine	46,761
50	Oregon	44,682
51	Vermont	37,255
52	Hawaii	23,438

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	393
2	Rhode Island	318
3	Maine	250
4	Alaska	234
5	Washington	226
6	Connecticut	218
7	North Carolina	217
8	Minnesota	197
9	Colorado	194
10	West Virginia	193
11	Pennsylvania	192
12	Illinois	179
13	Nevada	178
14	Florida	173
15	Wyoming	170
16	New Jersey	169
17	Delaware	168
18	Oregon	167
19	New Mexico	166
20	North Dakota	163
21	Louisiana	159
22	South Dakota	155
23	Tennessee	154
24	Idaho	147
25	Montana	145
26	Kentucky	138
27	Indiana	134
28	New Hampshire	123
29	New York	120
30	Massachusetts	117
31	Puerto Rico	117
32	Wisconsin	113
33	Kansas	112
34	Iowa	110
35	Ohio	106
36	Texas	106
37	Nebraska	102
38	South Carolina	101
39	Utah	100
40	Georgia	96
41	Arizona	95
42	Maryland	93
43	Mississippi	90
44	Missouri	90
45	District of Columbia	87
46	Virginia	86
47	Oklahoma	80
48	Arkansas	72
49	Vermont	64
50	Alabama	57
51	California	49
52	Hawaii	46

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,892
2	New York	2,673
3	Massachusetts	2,561
4	Rhode Island	2,531
5	Mississippi	2,426
6	Arizona	2,385
7	Connecticut	2,278
8	Louisiana	2,241
9	South Dakota	2,233
10	Alabama	2,229
11	Pennsylvania	2,065
12	North Dakota	2,005
13	Indiana	1,986
14	New Mexico	1,949
15	Illinois	1,928
16	Michigan	1,911
17	Arkansas	1,906
18	Iowa	1,889
19	South Carolina	1,852
20	Georgia	1,849
21	Nevada	1,781
22	Tennessee	1,776
23	Texas	1,745
24	Kansas	1,730
25	Oklahoma	1,726
26	Delaware	1,671
27	Ohio	1,654
28	Florida	1,651
29	District of Columbia	1,571
30	California	1,570
31	Missouri	1,518
32	West Virginia	1,510
33	Kentucky	1,490
34	Montana	1,476
35	Maryland	1,457
36	Wisconsin	1,304
37	Minnesota	1,291
38	Virginia	1,270
39	Nebraska	1,226
40	Wyoming	1,226
41	North Carolina	1,215
42	Idaho	1,149
43	Colorado	1,122
44	New Hampshire	961
45	Washington	733
46	Puerto Rico	731
47	Utah	692
48	Oregon	598
49	Maine	588
50	Alaska	452
51	Vermont	397
52	Hawaii	340

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	6
2	Florida	3
3	Missouri	3
4	New Jersey	3
5	Oklahoma	3
6	Pennsylvania	3
7	Rhode Island	3
8	South Dakota	3
9	West Virginia	3
10	Connecticut	2
11	Illinois	2
12	Louisiana	2
13	Maryland	2
14	Mississippi	2
15	Nevada	2
16	New Mexico	2
17	New York	2
18	North Carolina	2
19	Puerto Rico	2
20	Virginia	2
21	Alabama	1
22	Arkansas	1
23	California	1
24	Colorado	1
25	District of Columbia	1
26	Georgia	1
27	Idaho	1
28	Indiana	1
29	Kansas	1
30	Kentucky	1
31	Massachusetts	1
32	Minnesota	1
33	Montana	1
34	Nebraska	1
35	North Dakota	1
36	Ohio	1
37	South Carolina	1
38	Tennessee	1
39	Texas	1
40	Utah	1
41	Washington	1
42	Wisconsin	1
43	Wyoming	1
44	Alaska	0
45	Arizona	0
46	Delaware	0
47	Hawaii	0
48	Iowa	0
49	Maine	0
50	New Hampshire	0
51	Oregon	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	363,253	1	99
Crowley	Colorado	360,172	2	99
Bent	Colorado	273,624	3	99
Dewey	South Dakota	249,830	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,786	187	94
Richland	South Carolina	111,322	980	68
York	South Carolina	110,069	1038	66
Orange	California	85,180	2198	30
Pierce	Washington	55,652	2867	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,193	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,568	1823	41
York	South Carolina	1,345	2085	33
Davidson	Tennessee	1,343	2089	33
Richland	South Carolina	1,323	2112	32
Pierce	Washington	724	2741	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.